Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision

2019 ◽  
Vol 104 (1-4) ◽  
pp. 1369-1379 ◽  
Author(s):  
Pauline Ong ◽  
Woon Kiow Lee ◽  
Raymond Jit Hoo Lau
Keyword(s):  
Neural Network ◽  
Machine Vision ◽  
Condition Monitoring ◽  
End Milling ◽  
Wavelet Neural Network ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
Cnc End Milling

A Hybrid Model for Tool Condition Monitoring and Optimal Tool Management

2004 ◽  
Vol 471-472 ◽  
pp. 865-870 ◽  
Author(s):  
Ying Xue Yao ◽  
Y. Lu ◽  
Zhe Jun Yuan ◽  
J.Y. Hu
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Condition Monitoring ◽  
End Milling ◽  
Tool Condition Monitoring ◽  
Fuzzy Classification ◽  
Validity And Reliability ◽  
Tool Management ◽  
Tool Condition ◽  
Tool Replacement

This paper introduces a new hybrid model for tool condition monitoring (TCM) and optimal tool management (OTM) in end milling operation. The model includes a wavelet fuzzy neural network with acoustic emission (AE) and a model of fuzzy classification of tool wear state with the detected cutting parameters supported by cutting database. The results estimated by cutting conditions and detected signals are fused by artificial neural network (ANN) so as to facilitate effective tool replacement at a proper state or time. The validity and reliability of the method are verified by experimental results.

Tool Condition Monitoring Using Artificial Neural Network Models

2022 ◽  
pp. 400-426
Author(s):  
Srinivasa P. Pai ◽  
Nagabhushana T. N.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Medium Carbon Steel ◽  
Tool Condition Monitoring ◽  
Training Time ◽  
Tool Condition ◽  
Cell Structures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Tool wear is a major factor that affects the productivity of any machining operation and needs to be controlled for achieving automation. It affects the surface finish, tolerances, dimensions of the workpiece, increases machine down time, and sometimes performance of machine tool and personnel are affected. This chapter deals with the application of artificial neural network (ANN) models for tool condition monitoring (TCM) in milling operations. The data required for training and testing the models studied and developed are from live experiments conducted in a machine shop on a widely used steel, medium carbon steel (En 8) using uncoated carbide inserts. Acoustic emission data and surface roughness data has been used in model development. The goal is for developing an optimal ANN model, in terms of compact architecture, least training time, and its ability to generalize well on unseen (test) data. Growing cell structures (GCS) network has been found to achieve these requirements.

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